




'''simple docstring'''

import unittest

from transformers import DonutProcessor


a       :						Union[str, Any]								=		'''naver-clova-ix/donut-base'''



class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		DonutProcessor.from_pretrained(a_							)






			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		{
						    "name": "John Doe",
						    "age": "99",
						    "city": "Atlanta",
						    "state": "GA",
						    "zip": "30301",
						    "phone": "123-4567",
						    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
						}

						__snake_case			=		(
						    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
						    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
						    "<s_nicknames><s_nickname>Johnny</s_nickname>"
						    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
						)
						__snake_case			=		self.processor.tokenajson(a_							)

						self.assertDictEqual(a_      ,		a_							)






import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)